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368 results for “Consensus”
FIGURE 3. Bayesian majority-rule consensus tree showing results from a in New species of Mouse Spiders (Araneae: Mygalomorphae: Actinopodidae: Missulena) from the Pilbara region, Western Australia
FIGURE 3. Bayesian majority-rule consensus tree showing results from a partitioned phylogenetic analysis of the COI mtDNA dataset (25 taxa, 826 bp). Species described in this study are colour-coded in orange. The three major uncovered lineages are shaded light blue, green and red. Posterior probabilities are calculated in %. Bioregions (see Fig. 2A): AW – Avon Wheatbelt, GAS – Gascoyne, OVP – Ord Victoria Plain, JAR – Jarrah Forest, MUR – Murchison.
FIGURE 4. Majority-rule consensus tree derived from a in Revision and phylogeny of narrow-mouthed treefrogs (Cophyla) from northern Madagascar: integration of molecular, osteological, and bioacoustic data reveals three new species
FIGURE 4. Majority-rule consensus tree derived from a partitioned Bayesian inference analysis of concatenated DNA sequences of the 12S, 16S, COX1, COB, RAG1, KIAA1239, SACS, and TTN genes (6244 bp), showing relationships among species of the Cophylinae. Numbers at nodes are posterior probabilities (first number; values>0.95 bold) and maximum parsimony bootstrap values in percent (second value;>70% bold). The grey box highlights the included species of the genera Cophyla and Platypelis, which form two highly supported and reciprocally monophyletic groups.
FIGURE 7. Majority consensus Bayesian tree generated from partial cytochrome b in Description of a new species of the Miniopterus aelleni group (Chiroptera: Miniopteridae) from upland areas of central and northern Madagascar
FIGURE 7. Majority consensus Bayesian tree generated from partial cytochrome b sequence (725 bp), illustrating phylogenetic position of Miniopterus ambohitrensis sp. nov. Values at nodes represent Bayesian posterior probability followed by maximum likelihood (ML) bootstrap support. An asterisk (*) indicates that the node was fully supported in both the Bayesian and ML analyses, i.e., posterior probability 0.95 or greater and a bootstrap support value 85 or greater. The first value at the node is the posterior probability (Bayesian); the second is the bootstrap value derived from the maximum likelihood analysis (ML). The Bayesian analysis was run using MrBayes 3.2 (Huelsenbeck & Ronquist 2001; Ronquist et al. 2012) for 2,000,000 generations. The ML analysis was run using Garli 2.01 (Zwickl 2006) with bootstrap replicates set to 1,000. The nucleotide substitution model HKY was applied. Specimens obtained from type specimens are indicated by bolding and shading.
FIGURE 3. Majority-rule consensus tree derived from a in Revision and phylogeny of narrow-mouthed treefrogs (Cophyla) from northern Madagascar: integration of molecular, osteological, and bioacoustic data reveals three new species
FIGURE 3. Majority-rule consensus tree derived from a partitioned Bayesian inference analysis of DNA sequences of the nuclear RAG1 gene (503 bp), showing relationships among species of Cophyla. Numbers at nodes are posterior probabilities (only values>0.9 shown). The tree was rooted with the same outgroups as in Fig. 2 (removed for better graphical representation).
Figure 3. Strict consensus trees from the constrained analyses. First constrained analysis forcing a in Taxonomic, palaeobiological and evolutionary implications of a phylogenetic hypothesis for Ornithischia (Archosauria: Dinosauria)
Figure 3. Strict consensus trees from the constrained analyses. First constrained analysis forcing a monophyletic Silesauridae apart from the 'traditional ornithischians'. Abbreviations: Aphan, Aphanosauria; Herrer, Herrerasauridae. Silhouettes are based on artwork by Márcio L. Castro, Gabriel Lio, Rodrigo T. Müller, Maurício S. Garcia, John Sibbick and Douglas M. Heman.
FIGURE 13.Maximum Likelihood consensus tree generated from the 28S in A revision of the genus Isotomurus (Collembola: Isotomidae) in northern Iran using molecular evidence
FIGURE 13.Maximum Likelihood consensus tree generated from the 28S gene dataset with the GTR+I+G model. Bootstrap values more than 60% are given for appropriate clades; newly obtained sequences are in bold letters, others are from GenBank (NCBI).
Characterizing the consensus residue specificity and surface of Bcl-2 binding to BH3 ligands using the knob-socket model
<p><span>Cancer cells bypass cell death by changing the expression of the BCL-2 family of proteins, which are apoptotic pathway regulators. Upregulation of pro-survival BCL-2 proteins or downregulation of cell death effectors BAX and BAK interferes with the initiation of the intrinsic apoptotic pathway. In normal cells, apoptosis can occur through pro-apoptotic BH3-only proteins interacting and inhibiting pro-survival BCL-2 proteins. When cancer cells over-express pro-survival BCL-2 proteins, a potential remedy is the sequestration of these pro-survival proteins through a class of anti-cancer drugs called BH3 mimetics that bind in the hydrophobic groove of pro-survival BCL-2 proteins. To improve the design of these BH3 mimetics, the packing interface between BH3 domain ligands and pro-survival BCL-2 proteins was analyzed using the Knob-Socket model to identify the amino acid residues responsible for interaction affinity and specificity. A Knob-Socket analysis organizes all the residues in a binding interface into simple 4 residue units: 3-residue sockets defining surfaces on a protein that pack a 4th residue knob from the other protein. In this way, the position and composition of the knobs packing into sockets across the BH3/BCL-2 interface can be classified. A Knob-Socket analysis of 19 BCL-2 protein and BH3 helix co-crystals reveal multiple conserved binding patterns across protein paralogs. Conserved knob residues such as a Gly, Leu, Ala and Glu most likely define binding specificity in the BH3/BCL-2 interface, whereas other residues such as Asp, Asn, and Val are important for forming surface sockets that bind these knobs. These findings can be used to inform the design of BH3 mimetics that are specific to pro-survival BCL-2 proteins for cancer therapeutics.</span></p>
Datasets used in Consensus Clustering Problem in Single-cell Transcriptome Data Analysis
<p>20 benchmark scRNA-seq datasets used in Consensus Clustering Problem in Single-cell Transcriptome Data Analysis. In every datasets .zip files, it provided raw data files, the processed R code and the corresponding R objects. The datasets.xlsx file provided the detailed information of 20 datasets.</p>
Selective and deceptive citation in the construction of dueling consensuses
<p>Five files are attached associated with the publication "Selective and deceptive citation in the construction of dueling consensuses."<br> <br> The first, Analyze_Data.py, is Python code necessary for replicating this work. Some packages may need to be downloaded, which you can find in the second file, requirements.txt.</p> <p>The third file consists of annotation data for this paper. It is an Excel file with several sheets. The first, "Twitter_URLs," consists of the Twitter URLs we analyzed in our paper, the number of times they were shared in our dataset, shorthand names we assigned to these URLs, the number of outgoing citations we annotated from these sources, and whether we considered them to be explaining the science behind the effectiveness of masks. "Twitter_Edges" contains our annotations of citations on Twitter. They are directed from our shorthand names for Twitter URLs to reference titles for the citations. Data on these reference titles can be found in the "Twitter_Sources" document. The Date columns marks the most recent publication date we annotated for this source. The sheet "Twitter_URLs_50Shares" is an extended list of all URLs in our dataset shared at least 50 times. This data is not analyzed in the paper, and is provided for reference. The sheet "CitingPolarity" provides our annotations of sources we considered to be generally skeptical of masks' effectiveness ("Anti") and those that had either a positive or neutral stance. The "Citation_Context" page provides example context sentences for the 5 papers were reference in Table 1. Finally, the "WOS_Edges" sheet provides citation annotation for sources we retrieved from the Web of Science. See our paper for additional details.<br> <br> The zipped file WOS_Raw.zip contains citations collected as November 18, 2021 from the Web of Science. It is required to run the Analzye_Data.py script. Additional details can be found in our paper. Finally, the "Beers_ConsensusNetworks_VisualizationWorkbook.gephi" file provides visualization data for the network visualization in Figure 1. It can be opened in the network program Gephi.<br> <br> Additional Notes: In the "Twitter_URLs" datasheet, the URL on Row 157 (https://wwwnc.cdc.gov/eid/article/26/5/19-0994_article) was often erroneously unwound into the error page found on Row 6 (https://wwwnc.cdc.gov/eid/404.html?aspxerrorpath=/eid/article/26/5/19-0994_article). Both reference the same article, linked to in Row 157.</p>
Fig. 5. Bayesian consensus tree inferred from 18S in Description of one new, and new data on two known, species of Enchodelus Thorne, 1939 (Dorylaimida: Nordiidae) from Iran
Fig. 5. Bayesian consensus tree inferred from 18S small under TVMef + I model (lnL = 1172.5674; AIC = 2355.1348; freqA = 0.2416; freqC = 0.1799; freqG = 0.2957; freqT = 0.2828; R(a) = 1.6906; R(b) = 8.6571; R(c) = 4.9786; R(d) = 0.5884; R(e) = 8.6571; R(f) = 1; Pinva = 0.6319; Shape = equal). Posterior probability values exceeding 50% are given on appropriate clades.
Fig. 4. Bayesian consensus tree inferred from 18S in Description of one new, and new data on two known, species of Enchodelus Thorne, 1939 (Dorylaimida: Nordiidae) from Iran
Fig. 4. Bayesian consensus tree inferred from 18S under GTR + I + G model (lnL = 5297.7065; AIC = 10 615.4131; freqA = 0.2752; freqC = 0.2114; freqG = 0.2621; freqT = 0.2512; R(a) = 1.8482; R(b) = 5.0147; R(c) = 2.8536; R(d) = 0.2499; R(e) = 11.081; R(f) = 1; Pinva = 0.2089; Shape = 0.7502). Posterior probability values exceeding 50% are given on appropriate clades.
Identifying science-policy consensus regions of high biodiversity value and institutional recognition
<p>We retrieved 63 articles presenting prioritization maps (out of 5137 screened) and grouped these into three separate clusters based on their underlying methodology and input data, using Multivariate Component Analysis and Hierarchical Clustering on Principal Components. By combining these maps, weighted according to their cluster characteristics, we generated a map of scientific-consensus regions with the highest overlap of independently generated biodiversity priorities. We also created a map of policy-consensus, representing regions with the highest potential to attract the interest of the international conservation organization.</p> <p>Here we made available the raster version of of scientific-consensus regions and the map of policy-consensus.</p> <p>The maps are two raster of continuos value, from a minimum of 0 to a max which depend on the number of overlapping priority conservation maps</p> <p>Detailed methods are available from Cimatti et al. (2021) <a href="https://doi.org/10.1016/j.gecco.2021.e01938">https://doi.org/10.1016/j.gecco.2021.e01938</a></p> <p>In the zip folder you can find scientific-consensus of policy-consensus maps with original values and reclassified with percenitles</p>
Figure 9. Strict consensus tree from the 24 in Systematic revision of the species of Protypotherium (Notoungulata: Interatheriidae) from the Santa Cruz Formation (Early-Middle Miocene), Argentinian Patagonia: a new phylogenetic hypothesis for the Interatheriidae
Figure 9. Strict consensus tree from the 24 MPTs (331 steps) obtained under equally weighted characters. Numbers enclosed in boxes indicate Bremer support (above) and Bootstrap resampling frequencies (below; absolute: top, and GC: boưom).
Consensus Structural and functional connectome from 70 young healthy adults
<p>Consensus Structural and functional connectome from 70 young healthy adults.</p>
Expert Consensus Statements for the Management of a Physiologically Difficult Airway Using the Delphi Method (PDADelphi)
ClinicalTrials.gov study NCT05762068. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Dermoscopy Augmented Histology Trial, Consensus Agreement Diagnosis Made by Dermatopathology Experts
ClinicalTrials.gov study NCT05712551. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Hepatitis C Treatment Naive Genotype 1 Consensus Interferon Trial
ClinicalTrials.gov study NCT00211692. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Checklist for AI in Medical Imaging (CLAIM) Consensus Panel
ClinicalTrials.gov study NCT05984082. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Population Compared to Purposive Sampling for Consensus in an Online Delphi Study
ClinicalTrials.gov study NCT03505450. IPD Sharing: YES. Countries: 1. Publications: 5.
Delphi Consensus Excessive Daytime Sleepiness in OSA
ClinicalTrials.gov study NCT05055271. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.